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In computing, data validation or input validation is the process of ensuring data has undergone data cleansing to confirm it has data quality, that is, that it is ...
The original information may or may not appear literally in the encoded output; codes that include the unmodified input in the output are systematic, while those that do not are non-systematic. A simplistic example of ECC is to transmit each data bit three times, which is known as a (3,1) repetition code .
The validation test consists of comparing outputs from the system under consideration to model outputs for the same set of input conditions. Data recorded while observing the system must be available in order to perform this test. [3] The model output that is of primary interest should be used as the measure of performance. [1]
Every time the output of a process correctly implements its input specification, the software product is one step closer to final verification. If the output of a process is incorrect, the developers have not correctly implemented some component of that process. This kind of verification is called "artifact or specification verification".
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Checksums and hash functions, combined with the input data, can be viewed as systematic error-detecting codes. Linear codes are usually implemented as systematic error-correcting codes (e.g., Reed-Solomon codes in CDs). Convolutional codes are implemented as either systematic or non-systematic codes.
Verification is intended to check that a product, service, or system meets a set of design specifications. [6] [7] In the development phase, verification procedures involve performing special tests to model or simulate a portion, or the entirety, of a product, service, or system, then performing a review or analysis of the modeling results.
When accepting HTML input from users (say, <b>very</b> large), output encoding (such as <b>very</b> large) will not suffice since the user input needs to be rendered as HTML by the browser (so it shows as "very large", instead of "<b>very</b> large"). Stopping an XSS attack when accepting HTML input from users is much more complex ...